A knowledge graph to interpret clinical proteomics data

Implementing precision medicine hinges on the integration of omics data, such as proteomics, into the clinical decision-making process, but the quantity and diversity of biomedical data, and the spread of clinically relevant knowledge across multiple biomedical databases and publications, pose a challenge to data integration. Here we present the Clinical Knowledge Graph (CKG), an open-source platform currently comprising close to 20 million nodes and 220 million relationships that represent relevant experimental data, public databases and literature. The graph structure provides a flexible data model that is easily extendable to new nodes and relationships as new databases become available. The CKG incorporates statistical and machine learning algorithms that accelerate the analysis and interpretation of typical proteomics workflows. Using a set of proof-of-concept biomarker studies, we show how the CKG might augment and enrich proteomics data and help inform clinical decision-making

Consistency across multi-omics layers in a drug-perturbed gut microbial community

Multi-omics analyses are used in microbiome studies to understand molecular changes in microbial communities exposed to different conditions. However, it is not always clear how much each omics data type contributes to our understanding and whether they are concordant with each other. Here, we map the molecular response of a synthetic community of 32 human gut bacteria to three non-antibiotic drugs by using five omics layers (16S rRNA gene profiling, metagenomics, metatranscriptomics, metaproteomics and metabolomics). We find that all the omics methods with species resolution are highly consistent in estimating relative species abundances. Furthermore, different omics methods complement each other for capturing functional changes. For example, while nearly all the omics data types captured that the antipsychotic drug chlorpromazine selectively inhibits Bacteroidota representatives in the community, the metatranscriptome and metaproteome suggested that the drug induces stress responses related to protein quality control. Metabolomics revealed a decrease in oligosaccharide uptake, likely caused by Bacteroidota depletion. Our study highlights how multi-omics datasets can be utilized to reveal complex molecular responses to external perturbations in microbial communities

Detection potential of the KM3NeT detector for high-energy neutrinos from the Fermi bubbles

A recent analysis of the Fermi Large Area Telescope data provided evidence for a high-intensity emission of high-energy gamma rays with a E 2 spectrum from two large areas, spanning 50 above and below the Galactic centre (the ‘‘Fermi bubbles’’). A hadronic mechanism was proposed for this gamma-ray emission making the Fermi bubbles promising source candidates of high-energy neutrino emission. In this work Monte Carlo simulations regarding the detectability of high-energy neutrinos from the Fermi bubbles with the future multi-km3 neutrino telescope KM3NeT in the Mediterranean Sea are presented. Under the hypothesis that the gamma-ray emission is completely due to hadronic processes, the results indicate that neutrinos from the bubbles could be discovered in about one year of operation, for a neutrino spectrum with a cutoff at 100 TeV and a detector with about 6 km3 of instrumented volume. The effect of a possible lower cutoff is also considered.Published7–141.8. Osservazioni di geofisica ambientaleJCR Journalrestricte

The roles, training and knowledge of community health workers about diabetes and hypertension in Khayelitsha, Cape Town

Background: The current roles and capacity of community health workers (CHWs) in the management and control of non-communicable diseases (NCDs) remain poorly understood. Objectives: To assess CHWs’ current roles, training and knowledge about diabetes and hypertension in Khayelitsha, Cape Town. Methods: A cross-sectional study of 150 CHWs from two non-governmental organisations contracted to provide NCD care as part of a comprehensive package of services was conducted. An interviewer-administered closed-ended questionnaire was used to determine the roles, training, in-service support, knowledge and presence of NCDs. Descriptive analyses of these domains and multivariate analyses of the factors associated with CHWs’ knowledge of hypertension and diabetes were conducted. Results: The vast majority (96%) of CHWs were female, with a mean age of 35 years; 88% had some secondary schooling and 53% had been employed as CHWs for 4 years or more. Nearly half (47%) reported having an NCD. CHWs’ roles in NCDs included the delivery of medication, providing advice and physical assessment. Only 52% of CHWs reported some formal NCD-related training, while less than half of the trained CHWs (n = 35; 44%) had received follow-up refresher training. CHWs’ knowledge of diabetes and hypertension was poor. In the multivariate analyses, higher knowledge scores were associated with having an NCD and frequency of supervisory contact (≥1 per month). Conclusions: The roles performed by CHWs are broad, varied and essential for diabetes and hypertension management. However, basic knowledge about diabetes and hypertension remains poor while training is unstandardised and haphazard. These need to be improved if community-based NCD management is to be successful. The potential of peer education as a complementary mechanism to formal training needs as well as support and supervision in the workplace requires further exploration

The proteome landscape of the kingdoms of life.

Proteins carry out the vast majority of functions in all biological domains, but for technological reasons their large-scale investigation has lagged behind the study of genomes. Since the first essentially complete eukaryotic proteome was reported(1), advances in mass-spectrometry-based proteomics(2)have enabled increasingly comprehensive identification and quantification of the human proteome(3-6). However, there have been few comparisons across species(7,8), in stark contrast with genomics initiatives(9). Here we use an advanced proteomics workflow-in which the peptide separation step is performed by a microstructured and extremely reproducible chromatographic system-for the in-depth study of 100 taxonomically diverse organisms. With two million peptide and 340,000 stringent protein identifications obtained in a standardized manner, we double the number of proteins with solid experimental evidence known to the scientific community. The data also provide a large-scale case study for sequence-based machine learning, as we demonstrate by experimentally confirming the predicted properties of peptides fromBacteroides uniformis. Our results offer a comparative view of the functional organization of organisms across the entire evolutionary range. A remarkably high fraction of the total proteome mass in all kingdoms is dedicated to protein homeostasis and folding, highlighting the biological challenge of maintaining protein structure in all branches of life. Likewise, a universally high fraction is involved in supplying energy resources, although these pathways range from photosynthesis through iron sulfur metabolism to carbohydrate metabolism. Generally, however, proteins and proteomes are remarkably diverse between organisms, and they can readily be explored and functionally compared at www.proteomesoflife.org

Plasma proteome profiling discovers novel proteins associated with non-alcoholic fatty liver disease.

Non-alcoholic fatty liver disease (NAFLD) affects 25% of the population and can progress to cirrhosis with limited treatment options. As the liver secretes most of the blood plasma proteins, liver disease may affect the plasma proteome. Plasma proteome profiling of 48 patients with and without cirrhosis or NAFLD revealed six statistically significantly changing proteins (ALDOB, APOM, LGALS3BP, PIGR, VTN, and AFM), two of which are already linked to liver disease. Polymeric immunoglobulin receptor (PICR) was significantly elevated in both cohorts by 170% in NAFLD and 298% in cirrhosis and was further validated in mouse models. Furthermore, a global correlation map of clinical and proteomic data strongly associated DPP4, ANPEP, TGFBI, PIGR, and APOE with NAFLD and cirrhosis. The prominent diabetic drug target DPP4 is an aminopeptidase like ANPEP, ENPEP, and LAP3, all of which are up-regulated in the human or mouse data. Furthermore, ANPEP and TGFBI have potential roles in extracellular matrix remodeling in fibrosis. Thus, plasma proteome profiling can identify potential biomarkers and drug targets in liver disease

Integrative analysis of cell state changes in lung fibrosis with peripheral protein biomarkers.

The correspondence of cell state changes in diseased organs to peripheral protein signatures is currently unknown. Here, we generated and integrated single-cell transcriptomic and proteomic data from multiple large pulmonary fibrosis patient cohorts. Integration of 233,638 single-cell transcriptomes (n = 61) across three independent cohorts enabled us to derive shifts in cell type proportions and a robust core set of genes altered in lung fibrosis for 45 cell types. Mass spectrometry analysis of lung lavage fluid (n = 124) and plasma (n = 141) proteomes identified distinct protein signatures correlated with diagnosis, lung function, and injury status. A novel SSTR2+ pericyte state correlated with disease severity and was reflected in lavage fluid by increased levels of the complement regulatory factor CFHR1. We further discovered CRTAC1 as a biomarker of alveolar type-2 epithelial cell health status in lavage fluid and plasma. Using cross-modal analysis and machine learning, we identified the cellular source of biomarkers and demonstrated that information transfer between modalities correctly predicts disease status, suggesting feasibility of clinical cell state monitoring through longitudinal sampling of body fluid proteomes